Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

monetary is highly overall correlated with qtd_compras and 3 other fieldsHigh correlation
qtd_compras is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtd_items is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtd_prods is highly overall correlated with monetary and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_comprasHigh correlation
avg_ticket is highly overall correlated with unique_avg_basketHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
unique_avg_basket is highly overall correlated with qtd_prods and 1 other fieldsHigh correlation
avg_basket is highly overall correlated with monetary and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.1569664)Skewed
frequency is highly skewed (γ1 = 24.87687084)Skewed
qtd_returns is highly skewed (γ1 = 21.9754032)Skewed
customer_id has unique valuesUnique
recency has 33 (1.1%) zerosZeros
qtd_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-06-12 18:29:58.152200
Analysis finished2023-06-12 18:30:16.018943
Duration17.87 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:16.095501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1445
Coefficient of variation (CV)0.11258036
Kurtosis-1.2061782
Mean15270.377
Median Absolute Deviation (MAD)1489
Skewness0.032193711
Sum45322479
Variance2955457.9
MonotonicityStrictly increasing
2023-06-12T15:30:16.231664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12347 1
 
< 0.1%
16258 1
 
< 0.1%
16242 1
 
< 0.1%
16243 1
 
< 0.1%
16244 1
 
< 0.1%
16245 1
 
< 0.1%
16249 1
 
< 0.1%
16250 1
 
< 0.1%
16253 1
 
< 0.1%
16255 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

monetary
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.4851
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:16.367691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.465
Coefficient of variation (CV)3.7629558
Kurtosis397.30132
Mean2693.4851
Median Absolute Deviation (MAD)670.84
Skewness17.635372
Sum7994263.7
Variance1.0272766 × 108
MonotonicityNot monotonic
2023-06-12T15:30:16.492929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
889.93 2
 
0.1%
1353.74 2
 
0.1%
1025.44 2
 
0.1%
598.2 2
 
0.1%
745.06 2
 
0.1%
731.9 2
 
0.1%
2092.32 2
 
0.1%
178.96 2
 
0.1%
331 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65039.62 1
< 0.1%

qtd_compras
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7243935
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:16.632456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8577599
Coefficient of variation (CV)1.5473709
Kurtosis190.78624
Mean5.7243935
Median Absolute Deviation (MAD)2
Skewness10.765555
Sum16990
Variance78.45991
MonotonicityNot monotonic
2023-06-12T15:30:16.766032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 784
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtd_items
Real number (ℝ)

Distinct1670
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.1044
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:16.897543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.2914
Coefficient of variation (CV)3.6061408
Kurtosis516.7418
Mean1582.1044
Median Absolute Deviation (MAD)421
Skewness18.737654
Sum4695686
Variance32550350
MonotonicityNot monotonic
2023-06-12T15:30:17.041873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
246 8
 
0.3%
288 8
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
272 8
 
0.3%
200 7
 
0.2%
394 7
 
0.2%
Other values (1660) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%
50255 1
< 0.1%

qtd_prods
Real number (ℝ)

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76449
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:17.199397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.93294
Coefficient of variation (CV)2.1987868
Kurtosis354.77884
Mean122.76449
Median Absolute Deviation (MAD)44
Skewness15.706135
Sum364365
Variance72863.79
MonotonicityNot monotonic
2023-06-12T15:30:17.331379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2628
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.309299
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:17.477792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.760922
Coefficient of variation (CV)1.2091707
Kurtosis2.7765172
Mean64.309299
Median Absolute Deviation (MAD)26
Skewness1.7980529
Sum190870
Variance6046.7611
MonotonicityNot monotonic
2023-06-12T15:30:17.612393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.994257
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:17.749139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.118111
median17.953447
Q324.981794
95-th percentile90.052125
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.863683

Descriptive statistics

Standard deviation119.53207
Coefficient of variation (CV)3.6228143
Kurtosis812.96474
Mean32.994257
Median Absolute Deviation (MAD)5.9790186
Skewness25.156966
Sum97926.954
Variance14287.915
MonotonicityNot monotonic
2023-06-12T15:30:17.879310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
4.162 2
 
0.1%
15 2
 
0.1%
23.68131868 1
 
< 0.1%
57.87923077 1
 
< 0.1%
12.25581633 1
 
< 0.1%
8.021568627 1
 
< 0.1%
16.73351648 1
 
< 0.1%
15.70733333 1
 
< 0.1%
26.08797101 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.302133
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:18.010831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.917308
median48.267857
Q385.333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.416026

Descriptive statistics

Standard deviation63.505358
Coefficient of variation (CV)0.94358612
Kurtosis4.9080488
Mean67.302133
Median Absolute Deviation (MAD)26.267857
Skewness2.066084
Sum199752.73
Variance4032.9306
MonotonicityNot monotonic
2023-06-12T15:30:18.144635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
11 17
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
28 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11383237
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:18.285111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016339869
median0.025898352
Q30.049478583
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033138713

Descriptive statistics

Standard deviation0.40822056
Coefficient of variation (CV)3.5861552
Kurtosis989.06632
Mean0.11383237
Median Absolute Deviation (MAD)0.012196886
Skewness24.876871
Sum337.85449
Variance0.16664402
MonotonicityNot monotonic
2023-06-12T15:30:18.415768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.07692307692 13
 
0.4%
0.03571428571 13
 
0.4%
Other values (1215) 2635
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtd_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.888477
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:18.562571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.86478
Coefficient of variation (CV)8.107685
Kurtosis596.20199
Mean34.888477
Median Absolute Deviation (MAD)1
Skewness21.975403
Sum103549
Variance80012.486
MonotonicityNot monotonic
2023-06-12T15:30:18.692586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (203) 705
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

unique_avg_basket
Real number (ℝ)

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.489977
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:18.819868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.144643
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.477976

Descriptive statistics

Standard deviation15.460127
Coefficient of variation (CV)0.88394209
Kurtosis29.324685
Mean17.489977
Median Absolute Deviation (MAD)6.6
Skewness3.4364678
Sum51910.252
Variance239.01552
MonotonicityNot monotonic
2023-06-12T15:30:18.954671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
17 38
 
1.3%
14 38
 
1.3%
7 36
 
1.2%
11 36
 
1.2%
5 36
 
1.2%
15 35
 
1.2%
Other values (896) 2588
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

avg_basket
Real number (ℝ)

Distinct1978
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.25289
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-06-12T15:30:19.099509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.29167
Q3281.54808
95-th percentile599.58
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.31058

Descriptive statistics

Standard deviation283.8932
Coefficient of variation (CV)1.2016496
Kurtosis102.78169
Mean236.25289
Median Absolute Deviation (MAD)83.041667
Skewness7.7018777
Sum701198.57
Variance80595.347
MonotonicityNot monotonic
2023-06-12T15:30:19.227234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
73 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
197 7
 
0.2%
Other values (1968) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

Interactions

2023-06-12T15:30:14.257694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:58.388830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.882512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.384622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.731269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.201583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.683830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.137322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.526435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.046628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.487765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.839227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.389117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:58.537239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.009138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.492416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.851212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.325790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.802366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.254533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.643003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.167396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.600210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.957038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.497491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:58.674595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.133517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.604831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.967236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.445496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.916735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.364425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.760513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.284090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.704079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.071153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.609050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:58.785326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.253876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.700802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.079254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.559090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.044730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.470347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.871456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.393135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.810059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.176965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.732805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:58.909035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.374476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.827339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.211978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.703582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.175808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.595433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.000185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.521055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.930044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.297041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.864223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.035686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.512765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.951180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.352072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.827780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.305017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.720013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.138950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.641692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.044791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.422150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.987370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.157729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.642374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.083256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.473810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.954737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.420411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.836251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.260934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.763332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.173702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.542993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:15.092580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.275763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.773551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.183005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.585388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.066448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.535383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.941926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.386989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:10.874968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.278955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.653604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:15.212416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.401144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:00.898811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.295930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.710335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.194772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.655945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.061170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.536233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.007188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.403038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.806303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:15.326980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.537485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.035068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.408942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.835628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.316919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.779967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.185223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.673244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.126333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.519148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:13.925251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:15.435378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.643165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.151001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.513276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:03.948807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.437994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:06.892021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.287167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.799223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.237599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.622084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.039336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:15.556070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:29:59.761136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:01.265603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:02.617946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:04.076364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:05.558229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:07.011481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:08.412124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:09.915359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:11.364480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:12.727385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-12T15:30:14.150192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-12T15:30:19.347926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
customer_id1.000-0.0770.026-0.0710.0130.001-0.1310.019-0.002-0.064-0.016-0.123
monetary-0.0771.0000.7720.9250.746-0.4140.245-0.2490.0910.3710.1060.574
qtd_compras0.0260.7721.0000.7180.690-0.5030.060-0.2580.0780.295-0.1810.101
qtd_items-0.0710.9250.7181.0000.732-0.4070.166-0.2280.0810.3430.1480.729
qtd_prods0.0130.7460.6900.7321.000-0.436-0.377-0.1650.0350.2440.5150.384
recency0.001-0.414-0.503-0.407-0.4361.0000.0490.1090.017-0.1190.014-0.097
avg_ticket-0.1310.2450.0600.166-0.3770.0491.000-0.1230.0910.189-0.6180.187
avg_recency_days0.019-0.249-0.258-0.228-0.1650.109-0.1231.000-0.881-0.3980.131-0.078
frequency-0.0020.0910.0780.0810.0350.0170.091-0.8811.0000.235-0.1220.028
qtd_returns-0.0640.3710.2950.3430.244-0.1190.189-0.3980.2351.000-0.0530.209
unique_avg_basket-0.0160.106-0.1810.1480.5150.014-0.6180.131-0.122-0.0531.0000.404
avg_basket-0.1230.5740.1010.7290.384-0.0970.187-0.0780.0280.2090.4041.000

Missing values

2023-06-12T15:30:15.727000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-12T15:30:15.928942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
112347.04310.0072458182223.68131960.8333330.0191260.014.714286351.142857
212348.01437.2442332277553.23111194.3333330.0140850.05.250000583.000000
512352.01385.747526773617.99662343.3333330.02682063.08.14285775.142857
912356.02487.4331573582242.886724151.5000000.0098680.017.333333524.333333
1112358.0928.06224217154.591765149.0000000.0133330.06.000000121.000000
1212359.06372.58416222485725.69588764.8000000.01454510.053.500000405.500000
1312360.02302.06311561265218.27031774.0000000.0201340.034.666667385.333333
1512362.04737.23102197256318.50480524.3333330.03413017.020.000000219.700000
1612364.01208.104149981714.91481535.0000000.0377360.017.250000374.750000
1912370.03425.69423501665120.636687103.0000000.0129030.035.500000587.500000
customer_idmonetaryqtd_comprasqtd_itemsqtd_prodsrecencyavg_ticketavg_recency_daysfrequencyqtd_returnsunique_avg_basketavg_basket
431618269.0168.60176736624.0857148.0000001.0000006.07.00000076.000000
431718270.0283.152101113825.740909114.0000000.0087343.05.50000050.500000
431818272.03078.5862050166218.54566340.6666670.0244906.016.500000341.666667
431918273.0204.003803268.000000127.5000000.0117190.00.33333326.666667
432018274.0175.92188113015.99272713.0000001.00000088.011.00000088.000000
432118276.0335.861186144323.99000022.0000001.0000002.014.000000186.000000
432218277.0110.3816885813.797500260.0000001.0000001.08.00000068.000000
432618282.0178.05210312714.83750059.5000000.0166675.06.00000051.500000
432718283.02088.9316139575432.77046425.6923080.0477610.016.37500087.187500
432818287.01837.2831586704226.24685779.5000000.0187500.019.666667528.666667